A consultancy engagement that redesigns your costliest workflows around AI and guides the transition to an AI-native operating model: process visibility, adoption control, workflow redesign and governance, with people kept where their judgment matters.
AI Business Transformation is the Erbis consultancy engagement for operations leaders who have AI tools but no operating model around them. We map how work happens, inventory the AI in use, redesign two or three core workflows AI-first, and set up the governance, metrics and enablement that make the change stick. Engineering is available when a redesign needs software.
Futurum’s survey of 830 IT decision-makers shows the goal has moved: productivity as the desired AI outcome fell from 67.5% to 41.8% in a year, while direct financial impact nearly doubled. Yet MIT’s NANDA initiative found 95% of enterprise GenAI pilots show no measurable P&L return, and Deloitte reports only 20% of organizations achieve revenue growth from AI today. The gap is not tools. Thomson Reuters found AI meets expectations at 64% of firms with a strategy and 29% without one.
The usual state is bolt-on drift. Individuals draft with a chatbot, license counts pass for adoption, savings evaporate into rework, and 34% of professionals use tools nobody authorized. Nobody can see the process at task level, so nobody can redesign it. Governance arrives as a policy document after the fact, while EU AI Act high-risk obligations, now due 2 December 2027, and customers’ data-residency terms wait.
Erbis has built production AI since 2017 and the systems it runs inside since 2012. The engagement starts with visibility, because you cannot redesign what you cannot see. It ends with an operating model: owned workflows, adoption measured by depth rather than seats, value metrics that retire the old volume metrics, and governance you can show a regulator. Where a redesign needs software, the same team builds it. We do not run a change program that leaves when the consultants do.
Task-level mapping of how work actually happens, including the exceptions and the unwritten rules.
An inventory of the AI already in use, a data-handling policy you can enforce, and adoption measured by depth.
For each costly process, ask why a person does the work at all. Keep people where judgment and empathy matter; let systems carry the routine volume.
The structures that keep the change: owners, cadences and value metrics.
Governance that satisfies auditors, customers and regulators, built into the workflow rather than filed beside it.
Adoption is a skills problem before it is a tools problem.
Consultancy that can build. When a redesigned workflow needs an agent, an integration or a data pipeline, the same team ships it.
A transformation that depends on the consultants staying is a retainer. These four things are in place before we step back.
A written policy on which data classes may reach which tools, enforced technically where possible. A model and vendor inventory with risk ratings. Residency and retention documented per workflow. An owner named for each.
Every redesigned workflow has a baseline, a target and a measurement method agreed before redesign. AI steps carry an eval set from your own records and an acceptance threshold. Results are reviewed monthly; a workflow that misses its threshold is fixed or reverted.
Adoption depth by team and workflow, first-pass yield, rework rate, cycle time and cost per case on one dashboard. AI spend tracked per workflow, not per license. The dashboard is yours and built on your data stack.
Workflow owners, a review cadence and a governance forum are running before we step back. Playbooks, policies and enablement material are in your repositories. Erbis remains available for quarterly reviews and for engineering work under a separate, fixed-price scope.
28,800 hsaved per year
A custom-trained LLM with a verification controller generates Word contracts from 10,000+ precedents via an intranet chatbot, making drafting 10x faster.
$10M+annual savings across the companies using the solution
AWS Textract plus SageMaker Ground Truth human-in-the-loop review digitizes ~95% of invoices unattended, saving 10+ companies $10M+ a year.
60%faster insights with AI-driven visualizations and automated reporting
Embedded dashboards and natural-language Q&A on Redshift and QuickSight Q for a multi-tenant SaaS, with Keycloak SSO and row-level security per tenant.
40+US carriers aggregated in the platform
A San Francisco logistics SaaS re-architected to microservices on AWS in two years, with carrier rate comparison and portals used by Fortune 500 shippers.
Three to four weeks. Digital-maturity assessment, task-level mapping of the target processes, shadow-AI inventory and a value baseline. Uses the same questions as our AI readiness assessment, verified against evidence: interviews, adoption data, process and repository audits. Output: a scored snapshot, a prioritized roadmap and a fixed price for the first redesign phase.
Two or three core workflows rebuilt AI-first with the people who run them. Exceptions codified, human checkpoints placed, metrics attached. Where a workflow needs software, a 4-week pilot runs in parallel. Fix first, then automate: an inefficient process made faster is still inefficient.
Redesigned workflows go live team by team with role-based enablement, named owners and documentation. Adoption is measured by depth from the first week. Governance policies take effect alongside, not afterwards.
Monthly review of value metrics and adoption. Workflows that hit their threshold are extended along the value chain; those that miss are fixed. The governance forum meets on cadence. Erbis steps back to quarterly reviews at an agreed date.
Digital transformation moved processes from paper and email into systems; the shape of the work stayed the same, only faster. AI business transformation changes the shape: for each costly process it asks which steps still need a person and rebuilds the flow around that answer, with AI carrying the routine volume and people placed at the judgment points. It also adds an operating model, adoption measurement and governance, because AI without those becomes shadow tooling.
Five, in order. Visibility: task-level maps of how work happens, including exceptions. Control: an inventory of AI already in use and enforceable data rules. Redesign: two or three core workflows rebuilt AI-first with a baseline and a target. Governance: policy, model inventory, audit logs and regulatory readiness. Enablement: role-based skills, named owners and adoption measured by depth. Investment follows roughly 10/20/70 across algorithms, technology and people-and-process.
Whether they have run AI in production themselves, so the roadmap survives contact with real data and real systems. Whether they can build what they recommend, or hand you a deck and leave. Whether they measure adoption by depth rather than by licenses. Whether they resell licenses, which shapes their advice. And whether they will tell you what not to do; a good engagement removes projects from the list as often as it adds them.
Assessment takes three to four weeks and ends with a scored snapshot, a roadmap and a fixed price for the first redesign phase. A redesign phase covering two or three workflows runs eight to twelve weeks, with any software built in parallel as a 4-week pilot. Rollout and enablement follow team by team. Erbis steps back to quarterly reviews at an agreed date, usually within six to nine months; 89% of clients choose to keep working with us beyond 12 months.
Two roles. Data is what the redesigned workflows run on, so its readiness decides which processes can be rebuilt first; the assessment scores it per workflow. Data is also how the transformation is measured: adoption depth, first-pass yield, rework rate and cost per case come from operational data, not surveys. If the estate cannot supply those numbers, building the measurement layer becomes the first deliverable, often on the client’s existing AWS or Databricks stack.
We redesign work, and the decisions about roles stay with you. In practice the workflows we rebuild move people from re-keying, checking and chasing toward the exceptions, the judgment calls and the customer conversations. In the contract-automation case, 28,800 hours a year moved out of document assembly and the firm redeployed them to client work. We help you plan that redeployment deliberately and measure it, rather than letting the freed time evaporate into rework.
Take the 3-minute assessment to see where the gaps are. Then book a transformation session: 45 minutes, your processes, our questions, and a straight answer on where to start.
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